Integrative Windowing

One thing that happens frequently when using windowing with a rule
learning algorithm is that good rules have to be discovered again and
again in subsequent iterations of the windowing procedure. Although
correctly learned rules will add no more examples to the current
window, they have to be re-learned in the next iteration as long as
the current theory is not complete and consistent with the entire
training set. We have developed a new version of windowing, which
tries to exploit the fact that regions of the example space that are
already covered by good rules need not be further considered in
subsequent iterations. Because of its technique of successively
integrating learned rules into the final theory, we have named our
method Integrative Windowing.